class_docstr: A dataset that represents the unlabelled pool for active learning.
  
  Args:
      dataset (Dataset): A Dataset object providing unlabelled sample.
      make_unlabelled (Callable): The function that returns an
          unlabelled version of a datum so that it can still be used in the DataLoader.
code: def __init__(
      self,
      dataset: torchdata.Subset,
      make_unlabelled: Callable = _identity,
  ) -> None:
      self._dataset: torchdata.Subset = dataset
      self.make_unlabelled = make_unlabelled
  
imports: import warnings
  from copy import deepcopy
  from itertools import zip_longest
  from typing import Union, Optional, Callable, Any, Dict, List, Sequence, Mapping
  import numpy
  import torch
  import torch.utils.data
  from torch import Tensor
  from baal.active.dataset.base import SplittedDataset, Dataset
  from baal.utils.equality import deep_check
label_desc: Use image node_id 1 to create a new ActiveLearningPool object from inherited base classes: torchdata with example: obj = ActiveLearningPool(dataset, make_unlabelled)